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@article{179442,
        author = {Vaddeman Suresh and Mittapally Varsha and Mangali Sambhavana and Sapavath Yakub and Mrs.Zeenath jaha begum},
        title = {Adaptive Management of Sensitive and Non Sensitive in Digital Platform},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {8126-8135},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=179442},
        abstract = {This project addresses the growing challenge 
of content moderation across digital platforms by 
introducing an adaptive content management system. 
The system utilizes advanced Artificial Intelligence (AI) 
methods, including Natural Language Processing 
(NLP) and machine learning, to classify and moderate 
both textual and visual content in real-time. By 
applying NLP models like BERT, the system can detect 
harmful language such as hate speech, misinformation, 
and offensive remarks, ensuring that only appropriate 
content is allowed. For visual content, Convolutional 
Neural Networks (CNNs) are used to identify explicit or 
inappropriate images. One of the key innovations of the 
project is the adaptive feedback loop. Users can 
challenge moderation decisions, allowing the system to 
continuously improve based on real-time feedback. This 
approach makes the system not only effective but also 
responsive to evolving content patterns and user 
interactions. Designed to scale, the system can 
efficiently moderate large volumes of content, offering 
a robust solution for safe digital environments.},
        keywords = {Content Moderation, NLP, Text  Classification, Image Classification, Adaptive System,  Automated Governance.},
        month = {May},
        }